Heterogeneous Effects of Health Insurance on Rural Children’s Health in China: A Causal Machine Learning Approach
Abstract
:1. Introduction
2. Background
2.1. The Urban and Rural Resident Basic Medical Insurance (URRBMI)
2.2. Literature Review
3. Data and Descriptive Statistics
3.1. Data and Variables
3.2. Descriptive Statistics
4. Empirical Framework
4.1. Theoretical Basis
4.2. Propensity Score Matching (PSM)
4.3. Causal Forest
5. Results
5.1. The Average Treatment Effect
5.2. Heterogeneity Analysis
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Variables | Definition | Treatment Group | Control Group | Differences |
---|---|---|---|---|
Weight for Height Z-score (WHZ) | WHZ compares a child’s weight to the weight of a child of the same length/height and sex to classify nutritional status. | −0.185 (0.802) | −0.376 (0.770) | −0.191 *** |
Weight for Age Z-score (WAZ) | WAZ compares a child’s weight to the weight of a child of the same age and sex to classify nutritional status. | 0.022 (1.012) | −0.091 (0.929) | −0.112 ** |
Height by Age Z-score (HAZ) | HAZ compares a child’s height to the height of a child of the same age and sex to classify nutritional status. | 0.020 (0.991) | −0.069 (1.012) | −0.090 |
Body Mass Index (BMI) | Weight (kg)/height2 (m2) | 18.283 (5.209) | 18.944 (6.703) | 0.661 ** |
Child’s characteristics | ||||
Gender | 1 if male; 0 if female | 0.539 (0.499) | 0.501 (0.500) | −0.037 * |
Age | Age of the child | 7.813 (4.241) | 5.765 (4.807) | −2.048 *** |
Mother’s characteristics | ||||
Age | Age of the child’s mother | 34.211 (6.590) | 32.694 (6.895) | −1.517 *** |
Education | 1 if no formal education; 2 if primary school; 3 if junior high school; 4 if high school; 5 if junior college; 6 if bachelor degree; 7 if master. | 2.676 (1.143) | 3.057 (1.393) | 0.381 *** |
Family characteristics | ||||
Household income per capita | Logarithm of household income per capita | 8.994 (0.890) | 9.079 (1.059) | 0.085 ** |
Living area | 1 if urban area; 0 if rural area | 0.332 (0.471) | 0.455 (0.498) | 0.123 *** |
Geographic location | 1 if eastern region; 2 if central region; 3 if western region | 2.047 (0.819) | 1.816 (0.827) | −0.232 *** |
Dependent Variables | N | Nearest Neighbor Matching (1:1) | Nearest Neighbor Matching (1:4) | Nuclear Match |
---|---|---|---|---|
Weight for Height Z-score (WHZ) | 2015 | 0.199 *** (0.055) | 0.183 *** (0.046) | 0.189 *** (0.044) |
Weight for Age Z-score (WAZ) | 2015 | 0.155 ** (0.064) | 0.158 ** (0.056) | 0.131 ** (0.053) |
Height by Age Z-score (HAZ) | 2015 | 0.213 ** (0.072) | 0.168 ** (0.060) | 0.134 ** (0.057) |
Body Mass Index (BMI) | 3537 | −0.799 (0.468) | −0.634 (0.396) | −0.687 (0.375) |
Rank | Preschool Children | School-Age Children | ||
---|---|---|---|---|
Variable | Importance | Variable | Importance | |
1 | Age of the child’s mother | 0.368 | Mother’s education: no formal education | 0.263 |
2 | Age of the child | 0.158 | Age of the child’s mother | 0.200 |
3 | Mother’s education: no formal education | 0.155 | Age of the child | 0.149 |
4 | Mother’s education: bachelor degree | 0.060 | Wealth quantile 5 | 0.115 |
5 | Geographic location: central region | 0.036 | Living area: rural area | 0.099 |
6 | Mother’s education: primary school | 0.033 | Geographic location: central region | 0.036 |
7 | Child’s gender: male | 0.031 | Child’s gender: male | 0.029 |
8 | Wealth quantile 3 | 0.025 | Geographic location: western region | 0.026 |
9 | Living area: rural area | 0.022 | Mother’s education: junior college | 0.019 |
10 | Wealth quantile 2 | 0.021 | Wealth quantile 3 | 0.017 |
Preschool Children | School-Age Children | ||
---|---|---|---|
Subgroups | CATC | Subgroups | CATC |
Mother’s education: primary school | 0.214 ** (0.174) | Mother’s education: no formal education | −3.650 (1.290) |
Living area: rural area | 0.118 ** (0.090) | Living area: rural area | −1.835 (0.542) |
Living area: urban area | 0.140 (0.075) | Living area: urban area | 0.285 * (0.369) |
Child’s gender: male | 0.098 ** (0.236) | Child’s gender: male | −1.410 (0.610) |
Child’s gender: female | 0.139 (0.092) | Child’s gender: female | −0.423 (0.345) |
Geographic location: central region | 0.205 (0.106) | Geographic location: western region | −2.030 (1.030) |
Wealth quantile 2 | 0.218 * (0.152) | Wealth quantile 2 | −0.726 (0.568) |
Wealth quantile 5 | 0.160 (0.097) | Wealth quantile 4 | −1.243 (0.519) |
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Chen, H.; Xing, J.; Yang, X.; Zhan, K. Heterogeneous Effects of Health Insurance on Rural Children’s Health in China: A Causal Machine Learning Approach. Int. J. Environ. Res. Public Health 2021, 18, 9616. https://doi.org/10.3390/ijerph18189616
Chen H, Xing J, Yang X, Zhan K. Heterogeneous Effects of Health Insurance on Rural Children’s Health in China: A Causal Machine Learning Approach. International Journal of Environmental Research and Public Health. 2021; 18(18):9616. https://doi.org/10.3390/ijerph18189616
Chicago/Turabian StyleChen, Hua, Jianing Xing, Xiaoxu Yang, and Kai Zhan. 2021. "Heterogeneous Effects of Health Insurance on Rural Children’s Health in China: A Causal Machine Learning Approach" International Journal of Environmental Research and Public Health 18, no. 18: 9616. https://doi.org/10.3390/ijerph18189616